The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is...The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.展开更多
A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded ac...A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality. Positions and velocities of the satellites were predicted by a GMDH neural network, and the pseudo ranges and pseudo range rates received by the GPS receiver were simulated to ensure the regular op eration of the GPS/SINS Kalman filter during outages. In the mathematical simulation a tightly cou pled navigation system with a proposed approach has better navigation accuracy during GPS outages, and the anti jamming ability is strengthened for the tightly coupled navigation system.展开更多
传统的数据处理群方法(Group method of data handling,GMDH)在结构上具有自组织和全局选优的特性,非常适合进行非线性数据的拟合。但由于在传统GMDH网络建模是用最小二乘法来辨识参数,常常使得模型预测效果不理想。遗传算法是一种有效...传统的数据处理群方法(Group method of data handling,GMDH)在结构上具有自组织和全局选优的特性,非常适合进行非线性数据的拟合。但由于在传统GMDH网络建模是用最小二乘法来辨识参数,常常使得模型预测效果不理想。遗传算法是一种有效的搜索和优化方法,它具有自适应搜索、渐进式寻优、并行式搜索、通用性强等特点,论文将遗传算法引入GMDH网络,用遗传算法辨识部分描述式的系数,建立了基于遗传算法的GMDH网络模型。并将该模型应用于一组实测时间序列的预测研究,计算机仿真结果表明,模型预测效果令人满意。展开更多
本文提出基于改进自组织方法的GMDH(Group Method of Data Handling)型神经网络并将它应用于混沌预测。一般的GMDH型神经网络的自组织功能是通过给定一个准则阈值来确定或直接给定数值来实现,但GMDH型神经网络的自组织准则的阈值难以合...本文提出基于改进自组织方法的GMDH(Group Method of Data Handling)型神经网络并将它应用于混沌预测。一般的GMDH型神经网络的自组织功能是通过给定一个准则阈值来确定或直接给定数值来实现,但GMDH型神经网络的自组织准则的阈值难以合适确定,由此提出了一种简单的自组织方法来实现真正意义上的自组织功能。这种用改进了的自组织方法所构成的GMDH型神经网络可以应用于混沌时间序列预测。通过仿真实验,证明其预测效果明显比基本的GMDH型神经网络好,即改进GMDH型神经网络优于基本的GMDH型神经网络。展开更多
采用数据分组处理(Group Method of Data Handing,GMDH)的神经网络分类方法,建立4190ZLC船用四冲程增压柴油机性能预测的数学模型.针对船用中速柴油机运行状况,考虑到其影响运行状态的因素,结合实验数据进行4190ZLC船用柴油机性能的预...采用数据分组处理(Group Method of Data Handing,GMDH)的神经网络分类方法,建立4190ZLC船用四冲程增压柴油机性能预测的数学模型.针对船用中速柴油机运行状况,考虑到其影响运行状态的因素,结合实验数据进行4190ZLC船用柴油机性能的预测及仿真分析.该模型解决了神经网络结构较大,计算耗时较长的问题.将该模型与BP(Back-Propagation,BP)前馈神经网络仿真结果进行比较,结果表明其仿真效果好于BP神经网络模型,并且该神经网络能较好地满足柴油机性能预测仿真的需求.展开更多
基金Sponsored by National Key Technology Research and Development in 11th Five Years Plan of China(2006BAE03A07)Fundamental Research Funds for Central University of China(FRF-AS-09-006B)
文摘The hybrid method composed of clustering and predicting stages is proposed to predict the endpoint phos- phorus content of molten steel in BOF （Basic Oxygen Furnace）. At the clustering stage, the weighted K-means is performed to generate some clusters with homogeneous data. The weights of factors influencing the target are calcu- lated using EWM （Entropy Weight Method）. At the predicting stage, one GMDH （Group Method of Data Handling） polynomial neural network is built for each cluster. And the predictive results from all the GMDH polynomial neural networks are integrated into a whole to be the result for the hybrid method. The hybrid method, GMDH polnomial neural network and BP neural network are employed for a comparison. The results show that the proposed hybrid method is effective in predicting the endpoint phosphorus content of molten steel in BOF. Furthermore, the hybrid method outperforms BP neural network and GMDH polynomial neural network.
文摘A tightly coupled GPS （ global positioning system ）/SINS （ strap down inertial navigation system） based on a GMDH （ group method of data handling） neural network was presented to solve the problem of degraded accuracy for less than four visible GPS satellites with poor signal quality. Positions and velocities of the satellites were predicted by a GMDH neural network, and the pseudo ranges and pseudo range rates received by the GPS receiver were simulated to ensure the regular op eration of the GPS/SINS Kalman filter during outages. In the mathematical simulation a tightly cou pled navigation system with a proposed approach has better navigation accuracy during GPS outages, and the anti jamming ability is strengthened for the tightly coupled navigation system.
文摘传统的数据处理群方法(Group method of data handling,GMDH)在结构上具有自组织和全局选优的特性,非常适合进行非线性数据的拟合。但由于在传统GMDH网络建模是用最小二乘法来辨识参数,常常使得模型预测效果不理想。遗传算法是一种有效的搜索和优化方法,它具有自适应搜索、渐进式寻优、并行式搜索、通用性强等特点,论文将遗传算法引入GMDH网络,用遗传算法辨识部分描述式的系数,建立了基于遗传算法的GMDH网络模型。并将该模型应用于一组实测时间序列的预测研究,计算机仿真结果表明,模型预测效果令人满意。
文摘本文提出基于改进自组织方法的GMDH(Group Method of Data Handling)型神经网络并将它应用于混沌预测。一般的GMDH型神经网络的自组织功能是通过给定一个准则阈值来确定或直接给定数值来实现,但GMDH型神经网络的自组织准则的阈值难以合适确定,由此提出了一种简单的自组织方法来实现真正意义上的自组织功能。这种用改进了的自组织方法所构成的GMDH型神经网络可以应用于混沌时间序列预测。通过仿真实验,证明其预测效果明显比基本的GMDH型神经网络好,即改进GMDH型神经网络优于基本的GMDH型神经网络。
文摘采用数据分组处理(Group Method of Data Handing,GMDH)的神经网络分类方法,建立4190ZLC船用四冲程增压柴油机性能预测的数学模型.针对船用中速柴油机运行状况,考虑到其影响运行状态的因素,结合实验数据进行4190ZLC船用柴油机性能的预测及仿真分析.该模型解决了神经网络结构较大,计算耗时较长的问题.将该模型与BP(Back-Propagation,BP)前馈神经网络仿真结果进行比较,结果表明其仿真效果好于BP神经网络模型,并且该神经网络能较好地满足柴油机性能预测仿真的需求.
